TensorFlow 2 version | View source on GitHub |
Calculates how often predictions matches labels.
tf.keras.metrics.BinaryAccuracy(
name='binary_accuracy', dtype=None, threshold=0.5
)
For example, if y_true
is [1, 1, 0, 0] and y_pred
is [0.98, 1, 0, 0.6]
then the binary accuracy is 3/4 or .75. If the weights were specified as
[1, 0, 0, 1] then the binary accuracy would be 1/2 or .5.
This metric creates two local variables, total
and count
that are used to
compute the frequency with which y_pred
matches y_true
. This frequency is
ultimately returned as binary accuracy
: an idempotent operation that simply
divides total
by count
.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
Usage:
m = tf.keras.metrics.BinaryAccuracy()
m.update_state([1, 1, 0, 0], [0.98, 1, 0, 0.6])
print('Final result: ', m.result().numpy()) # Final result: 0.75
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()])
Args | |
---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
threshold
|
(Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. |
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Computes and returns the metric value tensor.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
update_state
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates metric statistics.
y_true
and y_pred
should have the same shape.
Args | |
---|---|
y_true
|
The ground truth values. |
y_pred
|
The predicted values. |
sample_weight
|
Optional weighting of each example. Defaults to 1. Can be
a Tensor whose rank is either 0, or the same rank as y_true ,
and must be broadcastable to y_true .
|
Returns | |
---|---|
Update op. |